Abstract
ABSTRACT The present article provides a didactic presentation and extension of selected features of Pearl’s DAG-based approach to causal inference for researchers familiar with structural equation modeling. We illustrate key concepts using a cross-lagged panel design. We distinguish between (a) forecasts of the value of an outcome variable after an intervention and (b) predictions of future values of an outcome variable. We consider the mean level and variance of the outcome variable as well as the probability that the outcome will fall within an acceptable range. We extend this basic approach to include additive random effects, allowing us to distinguish between average effects of interventions and person-specific effects of interventions. We derive optimal person-specific treatment levels and show that optimal treatment levels may differ across individuals. We present worked examples using simulated data based on the results of a prior empirical study of the relationship between blood insulin and glucose levels.
Highlights
Psychologists have long distinguished between experimental designs in which participants are randomly assigned to an active treatment or a control treatment and passive observa tional designs in which the responses of participants are observed (e.g., Cronbach, 1957, 1975)
We present worked examples using simulated data based on the results of a prior empirical study of the relationship between blood insulin and glucose levels
We show how to calculate the variance of the forecasted value and the probability that the outcome variable attains a value within a predefined accepta ble range
Summary
Psychologists have long distinguished between experimental designs in which participants are randomly assigned to an active treatment or a control treatment and passive observa tional designs (i.e., non-experimental designs without a manipulation) in which the responses of participants are observed (e.g., Cronbach, 1957, 1975).
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